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基于融合信息(火用)的转子振动故障SVM诊断方法

艾延廷 陈潮龙 田晶 王志

艾延廷, 陈潮龙, 田晶, 王志. 基于融合信息(火用)的转子振动故障SVM诊断方法[J]. 航空动力学报, 2014, (10): 2464-2470. doi: 10.13224/j.cnki.jasp.2014.10.025
引用本文: 艾延廷, 陈潮龙, 田晶, 王志. 基于融合信息(火用)的转子振动故障SVM诊断方法[J]. 航空动力学报, 2014, (10): 2464-2470. doi: 10.13224/j.cnki.jasp.2014.10.025
AI Yan-ting, CHEN Chao-long, TIAN Jing, WANG Zhi. SVM diagnosis method of rotor vibration faults based on integration of information exergy[J]. Journal of Aerospace Power, 2014, (10): 2464-2470. doi: 10.13224/j.cnki.jasp.2014.10.025
Citation: AI Yan-ting, CHEN Chao-long, TIAN Jing, WANG Zhi. SVM diagnosis method of rotor vibration faults based on integration of information exergy[J]. Journal of Aerospace Power, 2014, (10): 2464-2470. doi: 10.13224/j.cnki.jasp.2014.10.025

基于融合信息(火用)的转子振动故障SVM诊断方法

doi: 10.13224/j.cnki.jasp.2014.10.025
基金项目: 

航空科学基金(2012ZB54007)

详细信息
    作者简介:

    艾延廷(1963- ),男,辽宁葫芦岛人,教授,博士,主要研究方向为航空发动机结构强度、振动分析与故障诊断.

  • 中图分类号: V231.92;TH165+.3

SVM diagnosis method of rotor vibration faults based on integration of information exergy

  • 摘要: 通过提取信息(火用)特征,提出基于融合信息(火用)的转子振动故障支持向量机(SVM)诊断方法.首先,在转子试验台上分别模拟转子不平衡、轴系不对中、转子裂纹和转子碰磨4种典型故障,采集这4种典型故障在多转速和多测点下的振动加速度信号;其次,提取基于时域的奇异谱熵和频域的功率谱熵的转子振动故障过程变化规律的信息(火用)特征;最后,将提取到的信息(火用)特征作为故障向量,建立SVM故障诊断模型,进而对转子振动故障进行诊断.实例诊断结果表明:将信息(火用)特征与支持向量机相结合进行转子振动故障诊断,诊断结果准确率达到了97%,有效地提高了故障诊断的准确率.

     

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出版历程
  • 收稿日期:  2013-06-20
  • 刊出日期:  2014-10-28

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